With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBo...In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.展开更多
Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection w...Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection was conducted first to keep those with improved fibre quality,and followed for high yields,a large proportion in the resultant populations was the same between selections based on Cottonspec predicted yarn quality and HVI-measured fibre properties.They both exceeded the selection based on FQI and Background The approach of directly testing yarn quality to define fibre quality breeding objectives and progress the selection is attractive but difficult when considering the need for time and labour.The question remains whether yarn prediction tools from textile research can serve as an alternative.In this study,using a dataset from three seasons of field testing recombinant inbred line population,Cottonspec,a software developed by the Commonwealth Scientific and Industrial Research Organisation(CSIRO)for predicting ring spun yarn quality from fibre properties measured by High Volume Instrument(HVI),was used to select improved fibre quality and lint yield in the population.The population was derived from an advanced generation inter-crossing of four CSIRO conventional commercial varieties.The Cottonspec program was able to provide an integrated index of the fibre qualities affecting yarn properties.That was compared with selection based on HVI-measured fibre properties,and two composite fibre quality variables,namely,fibre quality index(FQI),and premium and discount(PD)points.The latter represents the net points of fibre length,strength,and micronaire based on the Premiums and Discounts Schedule used in the market while modified by the inclusion of elongation.PD points.Conclusions The population contained elite segregants with improved yield and fibre properties,and Cottonspec predicted yarn quality is useful to effectively capture these elites.There is a need to further develop yarn quality prediction tools through collaborative efforts with textile mills,to draw better connectedness between fibre and yarn quality.This connection will support the entire cotton value chain research and evolution.展开更多
Much recent researches have demonstrated that the quality of freshly-harvested wheat could be improved during postharvest maturation by determinating the rheological properties.However,this process is time-consuming a...Much recent researches have demonstrated that the quality of freshly-harvested wheat could be improved during postharvest maturation by determinating the rheological properties.However,this process is time-consuming and complex.This study aimed to provide a rapid and convenient method for predicting wheat quality during postharvest maturation by use of Gluto Peak device.Farinograph and Extensograph were used to determine the rheological properties of four wheat samples(WT1,WT2,WT3,WT4)stored under different conditions(WT1:15℃,50%RH;WT2:20℃65%RH;WT3:28℃75%RH;WT4:35℃85%RH)for a total of 10 weeks,and Gluto Peak test was used to determine the gluten aggregation properties of the four samples.Correlation analysis was also conducted between the rheological properties and the gluten aggregation properties.Results of rheological properties showed that all Extensographic properties(dough extensibility,resistance,maximum resistance and area)of the four samples increased along with the storage time,and the Farinographic properties(water absorption,dough development time,dough stability time,and farinograph quality number(FQN))had the same tendency,indicating that the rheological properties were improved considerably with storage time extending.The Gluto Peak curves revealed that Peak Maximum Time(PMT),Brabender Equivalents Maximum(BEM)and Energy to Maximum Torque(En MT)of wheat flour of the four samples varied greatly,particularly the PMT and En MT of the samples WT3 and WT4 increased remarkably.Results of correlation analysis showed that En MT had significant correlation with water absorption and area(P<0.05)for sample WT1,and also showed significant correlation with dough development time(P<0.05)for sample WT2.For sample WT3,PMT was significantly correlated with the dough development time,extensibility,area(P<0.05),and FQN(P<0.01);and En MT was in significant correction with water absorption(P<0.01),and dough stability time,FQN,extensibility,maximum resistance and area(P<0.05).For sample WT4,both PMT and En MT had significant correction with area(P<0.05).The study indicated that the Gluto Peak test is effective in quality prediction for the freshly-harvested wheat during postharvest maturation,making it possible to realize rapid wheat quality detection and evaluation in storage period.展开更多
Artificial Neural Network(ANN)models have been extensively applied in the prediction of water resource variables,and Geographical Information System(GIS)includes powerful functions to visualize spatial data.In order t...Artificial Neural Network(ANN)models have been extensively applied in the prediction of water resource variables,and Geographical Information System(GIS)includes powerful functions to visualize spatial data.In order to provide an efficient tool for environmental assessment and management that combines the advantages of these two modules,a GIS-based ANN water quality prediction system was developed in the present study.The ANN module and ArcGIS Engine module,along with a dynamic database,were imbedded in the system,which integrates water quality prediction via the ANN model and spatial presentation of the model results.The structure of the ANN model could be modified through the graphical user interface to optimize the model performance.The developed system was applied to a real case study for the prediction of the total phosphorus concentration in the Lake Champlain area.The prediction results were verified with the monitoring data,and the performance of the developed model was further evaluated through graphical techniques and quantitative statistical methods.Overall,the developed system provided satisfactory prediction results,and spatial distribution maps of the predicted results were obtained,which coincided with the monitored values.The developed GIS-based ANN water quality prediction system could serve as an efficient tool for engineers and decision makers.展开更多
In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result...In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.展开更多
Air quality prediction is an important part of environmental governance.The accuracy of the air quality prediction also affects the planning of people’s outdoor activities.How to mine effective information from histo...Air quality prediction is an important part of environmental governance.The accuracy of the air quality prediction also affects the planning of people’s outdoor activities.How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention,pollution control and pollution early warning.In this paper,we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants.Firstly,the data of air pollutants in different cities are collected by a sliding window technology,and the data of different cities in the sliding window are clustered by Kohonen method to find the same tends in air pollutants.On this basis,combined with the weather data,we use the ReliefF method to extract the characteristics of climate factors that helpful for prediction.Finally,different types of air pollutants and corresponding extracted the characteristics of climate factors are used to train different sub models.The experimental results of different algorithms with different air pollutants show that this method not only improves the accuracy of air quality prediction,but also improves the operation efficiency.展开更多
Air pollution is a severe environmental problem in urban areas.Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution.As a classic time series f...Air pollution is a severe environmental problem in urban areas.Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution.As a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality prediction.However,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction performance.Though some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are required.In this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations.The proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SvD)to compress and denoise the expanded matrix.Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.展开更多
The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process s...The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.展开更多
The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was p...The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.展开更多
The quality of GIServices(QoGIS)is an important consideration for services sharing and interoperation.However,QoGIS is a complex concept and difficult to be evaluated reasonably.Most of the current studies have focuse...The quality of GIServices(QoGIS)is an important consideration for services sharing and interoperation.However,QoGIS is a complex concept and difficult to be evaluated reasonably.Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sensitivity subsequently resulting in the inaccurate QoGIS values.For intensive geodata and computation,GIServices are more sensitive to the location factor than general services.This paper proposes a location-aware GIServices quality prediction model via collaborative filtering(LAGCF).The model uses a mixed CF method based on time zone feature from the perspectives of both user and GIServices.Time zone is taken as the location factor and mapped into the prediction process.A time zone-adjusted Pearson correlation coefficient algorithm was designed to measure the similarity between the GIServices and the target,helping to identify highly similar GIServices.By adopting a coefficient of confidence in the final generation phase,the value of the QoGIS most similar to the target services will play a dominant role in the comprehensive result.Two series of experiments on large-scale QoGIS data were implemented to verify the effectivity of LAGCF.The results showed that LAGCF can improve the accuracy of QoGIS prediction significantly.展开更多
Aquaculture has long been a critical economic sector in Taiwan.Since a key factor in aquaculture production efficiency is water quality,an effective means of monitoring the dissolved oxygen content(DOC)of aquaculture ...Aquaculture has long been a critical economic sector in Taiwan.Since a key factor in aquaculture production efficiency is water quality,an effective means of monitoring the dissolved oxygen content(DOC)of aquaculture water is essential.This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality.Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality.Since aquaculture water quality depends on a continuous interaction among multiple factors,and the current state is correlated with the previous state,a model with time series is required.Therefore,this study used recurrent neural networks(RNNs)with sequential characteristics.Commonly used RNNs such as long short-term memory model and gated recurrent unit(GRU)model have a memory function that appropriately retains previous results for use in processing current results.To construct a suitable RNN model,this study used Taguchi method to optimize hyperparameters(including hidden layer neuron count,iteration count,batch size,learning rate,and dropout ratio).Additionally,optimization performance was also compared between 5-layer and 7-layer network architectures.The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study.The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%,root mean square error of 0.0638,and R-value of 0.9984.Therefore,thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data,which is essential for a timely response to water quality issues.This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company.Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value.展开更多
Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare se...Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time slice.In the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing values.In the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.展开更多
Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to pr...Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to predictwater quality due to its random and trend changes.Therefore,amethod of predicting water quality which combines Auto Regressive Integrated Moving Average(ARIMA)and clusteringmodelwas proposed in this paper.By taking thewater qualitymonitoring data of a certain river basin as a sample,thewater quality Total Phosphorus(TP)index was selected as the prediction object.Firstly,the sample data was cleaned,stationary analyzed,and white noise analyzed.Secondly,the appropriate parameters were selected according to the Bayesian Information Criterion(BIC)principle,and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting.Thirdly,the relationship between the precipitation and the TP index in themonitoring water field was analyzed by the K-means clusteringmethod,and the random incremental characteristics of precipitation on water quality changes were calculated.Finally,by combining with the trend component characteristics and the random incremental characteristics,the water quality prediction results were calculated.Compared with the ARIMA water quality prediction method,experiments showed that the proposed method has higher accuracy,and its Mean Absolute Error(MAE),Mean Square Error(MSE),and Mean Absolute Percentage Error(MAPE)were respectively reduced by 44.6%,56.8%,and 45.8%.展开更多
At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the p...At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.展开更多
Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predict...Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predicting effluent COD and NH3 levels.We employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 d.Then we collected data on critical parameters like COD,DO,pH,NH3,EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 points.Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this data.This innovative model synergized sewage imagery with water quality data,enhancing prediction accuracy.As a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data.Consequently,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.展开更多
Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in...Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.展开更多
Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands.However,the effe...Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands.However,the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction.To address this problem,in this study,we propose an approach based on multi-source data fusion that considers the following indicators:water quality indicators,water quantity indicators,and meteorological indicators.In this study,we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland:(1)multiple linear regression;(2)backpropagation neural network(BPNN);(3)genetic algorithm combined with the BPNN to solve the local minima problem;and(4)long short-term memory(LSTM)neural network to consider the influence of past results on the present.The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method,with a satisfactory R^(2).Additionally,given the huge fluctuation of different pollutant concentrations in the effluent,we used a moving average method to smooth the original data,which successfully improved the accuracy of traditional neural networks and hybrid neural networks.The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.展开更多
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
文摘In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.
基金funded through Cotton Breeding Australia,a Joint Venture between CSIRO and Cotton Seed Distributors(Wee Waa,NSW 2388,Australia)。
文摘Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection was conducted first to keep those with improved fibre quality,and followed for high yields,a large proportion in the resultant populations was the same between selections based on Cottonspec predicted yarn quality and HVI-measured fibre properties.They both exceeded the selection based on FQI and Background The approach of directly testing yarn quality to define fibre quality breeding objectives and progress the selection is attractive but difficult when considering the need for time and labour.The question remains whether yarn prediction tools from textile research can serve as an alternative.In this study,using a dataset from three seasons of field testing recombinant inbred line population,Cottonspec,a software developed by the Commonwealth Scientific and Industrial Research Organisation(CSIRO)for predicting ring spun yarn quality from fibre properties measured by High Volume Instrument(HVI),was used to select improved fibre quality and lint yield in the population.The population was derived from an advanced generation inter-crossing of four CSIRO conventional commercial varieties.The Cottonspec program was able to provide an integrated index of the fibre qualities affecting yarn properties.That was compared with selection based on HVI-measured fibre properties,and two composite fibre quality variables,namely,fibre quality index(FQI),and premium and discount(PD)points.The latter represents the net points of fibre length,strength,and micronaire based on the Premiums and Discounts Schedule used in the market while modified by the inclusion of elongation.PD points.Conclusions The population contained elite segregants with improved yield and fibre properties,and Cottonspec predicted yarn quality is useful to effectively capture these elites.There is a need to further develop yarn quality prediction tools through collaborative efforts with textile mills,to draw better connectedness between fibre and yarn quality.This connection will support the entire cotton value chain research and evolution.
基金financial support of National Natural Science Foundation of China(No.31771897)the Key Scientific Research Project of Universities in Henan Province(16A210018)the Focus on Fostering Basic Research Fund of the Henan University of Technology(2013JCYJ05)。
文摘Much recent researches have demonstrated that the quality of freshly-harvested wheat could be improved during postharvest maturation by determinating the rheological properties.However,this process is time-consuming and complex.This study aimed to provide a rapid and convenient method for predicting wheat quality during postharvest maturation by use of Gluto Peak device.Farinograph and Extensograph were used to determine the rheological properties of four wheat samples(WT1,WT2,WT3,WT4)stored under different conditions(WT1:15℃,50%RH;WT2:20℃65%RH;WT3:28℃75%RH;WT4:35℃85%RH)for a total of 10 weeks,and Gluto Peak test was used to determine the gluten aggregation properties of the four samples.Correlation analysis was also conducted between the rheological properties and the gluten aggregation properties.Results of rheological properties showed that all Extensographic properties(dough extensibility,resistance,maximum resistance and area)of the four samples increased along with the storage time,and the Farinographic properties(water absorption,dough development time,dough stability time,and farinograph quality number(FQN))had the same tendency,indicating that the rheological properties were improved considerably with storage time extending.The Gluto Peak curves revealed that Peak Maximum Time(PMT),Brabender Equivalents Maximum(BEM)and Energy to Maximum Torque(En MT)of wheat flour of the four samples varied greatly,particularly the PMT and En MT of the samples WT3 and WT4 increased remarkably.Results of correlation analysis showed that En MT had significant correlation with water absorption and area(P<0.05)for sample WT1,and also showed significant correlation with dough development time(P<0.05)for sample WT2.For sample WT3,PMT was significantly correlated with the dough development time,extensibility,area(P<0.05),and FQN(P<0.01);and En MT was in significant correction with water absorption(P<0.01),and dough stability time,FQN,extensibility,maximum resistance and area(P<0.05).For sample WT4,both PMT and En MT had significant correction with area(P<0.05).The study indicated that the Gluto Peak test is effective in quality prediction for the freshly-harvested wheat during postharvest maturation,making it possible to realize rapid wheat quality detection and evaluation in storage period.
基金Supported by the National Natural Science Foundation of China(Nos.41807247,41807229)the Special Fund for Shandong Post-doctoral Innovation Project。
文摘Artificial Neural Network(ANN)models have been extensively applied in the prediction of water resource variables,and Geographical Information System(GIS)includes powerful functions to visualize spatial data.In order to provide an efficient tool for environmental assessment and management that combines the advantages of these two modules,a GIS-based ANN water quality prediction system was developed in the present study.The ANN module and ArcGIS Engine module,along with a dynamic database,were imbedded in the system,which integrates water quality prediction via the ANN model and spatial presentation of the model results.The structure of the ANN model could be modified through the graphical user interface to optimize the model performance.The developed system was applied to a real case study for the prediction of the total phosphorus concentration in the Lake Champlain area.The prediction results were verified with the monitoring data,and the performance of the developed model was further evaluated through graphical techniques and quantitative statistical methods.Overall,the developed system provided satisfactory prediction results,and spatial distribution maps of the predicted results were obtained,which coincided with the monitored values.The developed GIS-based ANN water quality prediction system could serve as an efficient tool for engineers and decision makers.
基金National Natural Science Foundation of China(No.51175077)
文摘In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.
基金This research was supported in part by the National Natural Science Foundation of China under grant Nos.61602202 and 61603146the Natural Science Foundation of Jiangsu Province under contracts BK20160428 and BK20160427+1 种基金the Six talent peaks project in Jiangsu Province under contract XYDXX-034the project in Jiangsu Association for science and technology.
文摘Air quality prediction is an important part of environmental governance.The accuracy of the air quality prediction also affects the planning of people’s outdoor activities.How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention,pollution control and pollution early warning.In this paper,we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants.Firstly,the data of air pollutants in different cities are collected by a sliding window technology,and the data of different cities in the sliding window are clustered by Kohonen method to find the same tends in air pollutants.On this basis,combined with the weather data,we use the ReliefF method to extract the characteristics of climate factors that helpful for prediction.Finally,different types of air pollutants and corresponding extracted the characteristics of climate factors are used to train different sub models.The experimental results of different algorithms with different air pollutants show that this method not only improves the accuracy of air quality prediction,but also improves the operation efficiency.
文摘Air pollution is a severe environmental problem in urban areas.Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution.As a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality prediction.However,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction performance.Though some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are required.In this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations.The proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SvD)to compress and denoise the expanded matrix.Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.
基金supported by the National Natural Science Foundation of China (Grant No.51675418).
文摘The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.
基金financially supported by the National Key Research and Development Program of China(2022YFB3706800,2020YFB1710100)the National Natural Science Foundation of China(51821001,52090042,52074183)。
文摘The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.
基金National Natural Science Foundation of China[grant number 41401464]Open Foundation of LIESMARS[grant number 15I02]Natural Science Foundation of Hubei Province[grant number 2016CFC769].
文摘The quality of GIServices(QoGIS)is an important consideration for services sharing and interoperation.However,QoGIS is a complex concept and difficult to be evaluated reasonably.Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sensitivity subsequently resulting in the inaccurate QoGIS values.For intensive geodata and computation,GIServices are more sensitive to the location factor than general services.This paper proposes a location-aware GIServices quality prediction model via collaborative filtering(LAGCF).The model uses a mixed CF method based on time zone feature from the perspectives of both user and GIServices.Time zone is taken as the location factor and mapped into the prediction process.A time zone-adjusted Pearson correlation coefficient algorithm was designed to measure the similarity between the GIServices and the target,helping to identify highly similar GIServices.By adopting a coefficient of confidence in the final generation phase,the value of the QoGIS most similar to the target services will play a dominant role in the comprehensive result.Two series of experiments on large-scale QoGIS data were implemented to verify the effectivity of LAGCF.The results showed that LAGCF can improve the accuracy of QoGIS prediction significantly.
基金Publication costs are funded by the Ministry of Science and Technology,Taiwan,under Grant Numbers MOST 110-2221-E-153-010.
文摘Aquaculture has long been a critical economic sector in Taiwan.Since a key factor in aquaculture production efficiency is water quality,an effective means of monitoring the dissolved oxygen content(DOC)of aquaculture water is essential.This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality.Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality.Since aquaculture water quality depends on a continuous interaction among multiple factors,and the current state is correlated with the previous state,a model with time series is required.Therefore,this study used recurrent neural networks(RNNs)with sequential characteristics.Commonly used RNNs such as long short-term memory model and gated recurrent unit(GRU)model have a memory function that appropriately retains previous results for use in processing current results.To construct a suitable RNN model,this study used Taguchi method to optimize hyperparameters(including hidden layer neuron count,iteration count,batch size,learning rate,and dropout ratio).Additionally,optimization performance was also compared between 5-layer and 7-layer network architectures.The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study.The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%,root mean square error of 0.0638,and R-value of 0.9984.Therefore,thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data,which is essential for a timely response to water quality issues.This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company.Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value.
基金supported by the National Natural Science Foundation of China (No.61872002)the National Natural Science Foundation of Anhui Province of China (No.1808085MF197)the Philosophy and Social Science Planned Project of Anhui Province (No. AHSKY2015D67)
文摘Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time slice.In the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing values.In the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.
基金funded by the National Natural Science Foundation of China(No.51775185),Natural Science Foundation of Hunan Province(2022JJ90013)Scientific Research Fund of Hunan Province Education Department(18C0003)+1 种基金Research project on teaching reform in colleges and universities of Hunan Province Education Department(20190147)Hunan Normal University University-Industry Cooperation.This work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open project,Grant Number 20181901CRP04.
文摘Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to predictwater quality due to its random and trend changes.Therefore,amethod of predicting water quality which combines Auto Regressive Integrated Moving Average(ARIMA)and clusteringmodelwas proposed in this paper.By taking thewater qualitymonitoring data of a certain river basin as a sample,thewater quality Total Phosphorus(TP)index was selected as the prediction object.Firstly,the sample data was cleaned,stationary analyzed,and white noise analyzed.Secondly,the appropriate parameters were selected according to the Bayesian Information Criterion(BIC)principle,and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting.Thirdly,the relationship between the precipitation and the TP index in themonitoring water field was analyzed by the K-means clusteringmethod,and the random incremental characteristics of precipitation on water quality changes were calculated.Finally,by combining with the trend component characteristics and the random incremental characteristics,the water quality prediction results were calculated.Compared with the ARIMA water quality prediction method,experiments showed that the proposed method has higher accuracy,and its Mean Absolute Error(MAE),Mean Square Error(MSE),and Mean Absolute Percentage Error(MAPE)were respectively reduced by 44.6%,56.8%,and 45.8%.
基金the National Natural Science Foundation of China(No.51775185)Natural Science Foundation of Hunan Province(No.2022JJ90013)+1 种基金Intelligent Environmental Monitoring Technology Hunan Provincial Joint Training Base for Graduate Students in the Integration of Industry and Education,and Hunan Normal University University-Industry Cooperation.the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project,Grant Number 20181901CRP04.
文摘At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.
基金supported by the National Key R&D Program of China(No.2021YFC1809001).
文摘Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predicting effluent COD and NH3 levels.We employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 d.Then we collected data on critical parameters like COD,DO,pH,NH3,EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 points.Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this data.This innovative model synergized sewage imagery with water quality data,enhancing prediction accuracy.As a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data.Consequently,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.
基金This research was supported by the National Natural Science Foundation of China(Nos.41977300 and 41907297)the Science and Technology Program of Guangzhou(China)(No.202002020055)the Fujian Provincial Natural Science Foundation(China)(No.2020I1001).
文摘Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.
基金the Ministry of Education-China Mobile Research Foundation Project of China(MCM20180703)the National Key Research and Development Program of China(2020YFB1711100)for financial support.
基金funded by National Natural Science Foundation of China(No.51908161&52100044)Guangdong Basic and Applied Basic Research Foundation(No.2019A1515010807)+1 种基金State Key Laboratory of Urban Water Resource and Environment(Harbin Institute of Technology)(2021TS30)Shenzhen Science and Technology Program(No.KQTD20190929172630447,KCXFZ20211020163404007 and GXWD20201230155427003-20200824100026001).
文摘Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands.However,the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction.To address this problem,in this study,we propose an approach based on multi-source data fusion that considers the following indicators:water quality indicators,water quantity indicators,and meteorological indicators.In this study,we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland:(1)multiple linear regression;(2)backpropagation neural network(BPNN);(3)genetic algorithm combined with the BPNN to solve the local minima problem;and(4)long short-term memory(LSTM)neural network to consider the influence of past results on the present.The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method,with a satisfactory R^(2).Additionally,given the huge fluctuation of different pollutant concentrations in the effluent,we used a moving average method to smooth the original data,which successfully improved the accuracy of traditional neural networks and hybrid neural networks.The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.